Comprehensive molecular characterization of collecting duct carcinoma for therapeutic vulnerability

Collecting duct carcinoma (CDC) is an aggressive rare subtype of kidney cancer with unmet clinical needs. Little is known about its underlying molecular alterations and etiology, primarily due to its rarity, and lack of preclinical models. This study aims to comprehensively characterize molecular alterations in CDC and identify its therapeutic vulnerabilities. Through whole-exome and transcriptome sequencing, we identified KRAS hotspot mutations (G12A/D/V) in 3/13 (23%) of the patients, in addition to known TP53, NF2 mutations. 3/13 (23%) patients carried a mutational signature (SBS22) caused by aristolochic acid (AA) exposures, known to be more prevalent in Asia, highlighting a geologically specific disease etiology. We further discovered that cell cycle-related pathways were the most predominantly dysregulated pathways. Our drug screening with our newly established CDC preclinical models identified a CDK9 inhibitor LDC000067 that specifically inhibited CDC tumor growth and prolonged survival. Our study not only improved our understanding of oncogenic molecular alterations of Asian CDC, but also identified cell-cycle machinery as a therapeutic vulnerability, laying the foundation for clinical trials to treat patients with such aggressive cancer.


Introduction
Collecting duct carcinoma (CDC) is one of the most aggressive kidney cancer, which accounts for about 1% of all kidney cancers (Suarez et al, 2022).NCCN (National Comprehensive Cancer Network) Guidelines® for kidney cancer (version 1.2024) recommends surveillance or clinical trial, after CDC patients' radical/partial nephrectomy.Even though early surgical treatment can improve patient survival, CDC patients usually present with higher grade, and advanced stage, where as high as 70% of the patients present with metastases, having only median overall survival of 13 to 17 months, and 5-year cancer-specific survival of only 30% (Sui et al, 2017;Tang et al, 2021).There is no effective targeted therapy for CDC (Panunzio et al, 2023;Xie et al, 2022).Chemotherapy and immunotherapy were reported to achieve complete or partial responses; however, these treatments were primarily in case reports or small cohorts with limited benefits, highlighting the necessity of understanding the molecular basis of CDC (Dason et al, 2013;Funajima et al, 2023;Guillaume et al, 2022;Oudard et al, 2007;Procopio et al, 2022;Thibault et al, 2023;Yasuoka et al, 2018).Such unmet clinical need requires more comprehensive molecular characterizations of CDC.
Molecular alterations in CDC have been investigated through genome and transcriptome studies.Genomic characterizations of CDC are still ongoing with limited consensus.Pal et al, studied mutations in 17 CDC FFPE samples using targeted panels of cancer-related genes and found recurrent mutations in NF2 (29%), SETD2 (24%), SMARCB1 (18%), and CDKN2A (12%) (Pal et al, 2016).Wang et al,studied 7 CDCs but found no recurrent single nucleotide variant, except for MLL, but homozygous deletions of CDKN2A in three samples (Wang et al, 2016).Metastatic CDC harbored more mutations in SMARCB1, NF2, RB1, and RET, compared to clear cell renal cell carcinoma (ccRCC) (Bratslavsky et al, 2021).Transcriptome data for the CDC are similarly limited to a few studies on Caucasian patients.Malouf et al,profiled 11 CDC patients, identifying distal convoluted tubules as its cells of origins (Malouf et al, 2016).Wach et al, studied 2 CDCs, and found overexpression of KRT17, which correlated with poorer survival in ccRCC (Wach et al, 2019).Gargiuli et al, profiled six CDC samples and discovered two CDC subtypes with different cell signaling, metabolic and immune profiles (Gargiuli et al, 2021).Wang et al, found upregulation of SLC7A11, a cisplatin resistance-associated gene, in 80% (four out of five) of the CDC samples checked (Wang et al, 2016).Msaouel et al, profiled nine CDCs, but the study mainly focused on renal medullary carcinoma (Msaouel et al, 2020a).Frequent overexpression of HER2 protein was observed (23%, 6/26) (Costantini et al, 2020).Together, our understanding of transcriptomic dysregulation in the CDC remains limited, in part due to the small sample sizes and heterogeneity of the CDC.
Therefore, existing CDC studies have their limitations: (1) most data on CDC are for Caucasian patients, while CDC in Asians has not been molecularly characterized; (2) besides sample size, CDC therapeutics design is also hampered by the scarcity of preclinical models, including cell lines and patient-derived xenograft (PDX) models for drug screening and testing (Wu et al, 2009).
To address these limitations, in this study, we analyzed the genome and transcriptome in our new Asian CDC cohort (n = 14, 13 normal-matched tumors and 1 PDX model).Besides a new CDC cell line, to our knowledge, we are the first to establish a CDC PDX model for drug screening and testing, leading to the identification and successful validation of a CDK9 inhibitor (LDC000067) that specifically inhibited CDC growth, providing a potential therapeutic strategy for CDC treatment.

Results
Our Asian CDC cohort consisted of 14 patients, eight males and six females, with a mean age of 60.The majority (11/14) of the patients are Chinese, two were Japanese, and one with no ethnic information available.In total, 13 fresh-frozen normal-matched CDC tumors were analyzed by WES, eight of which were also profiled with RNA-seq.One PDX (CDC1) and its cell line models were established for drug screening and validations.(Appendix Tables S1, S2).
No clear association between patient demographics and mutation type was observed (Fig. 1C,D).The types of single base substitutions differ among patients, with T04, T05, and T10 mostly showing T > A mutations (Fig. 1D,E; Appendix Fig. S1H).Mutational signature analysis revealed these three patients carried mutational signature SBS22, caused by exposure to aristolochic acid (AA), a known Group 1 carcinogen (Fig. 1E).AA exposure is known to cause higher mutational load and more neoantigens, which was confirmed by our neoantigen analysis, where the number of strong binder neoantigens generally correlated with the mutational load (Fig. 1F).Other mutational signatures are associated with aging (SBS1 and SBS5), activity of AID/ APOBEC family of cytidine deaminases (SBS2 and SBS13), and occupational exposure to haloalkanes (SBS42).No focal amplification was identified whereas focal deletion was found at 9p21.3 (q value = 0.071), where CDKN2A and CDKN2B genes are located, though with marginal statistical significance.(Appendix Fig. S1I-K).Read coverage at the CDKN2A and CDKN2B loci showed that tumor samples had relatively lower coverage, as compared to the normal samples, supporting shallow deletions (Appendix Fig. S1L).However, focal deletion in the 9p21.3 did not reduce expression levels of CDKN2A or CDKN2B, as they were increased by 3.37 and 1.39 log2-folds in the tumors respectively (Appendix Fig. S1M,N).

Genomic mutation and associated transcriptomic alteration inform precision therapy
Leveraging our matched WES and RNA-seq data, we next investigated whether the frequently mutated Hippo and RTK-RAS pathways, as well as AA mutations, lead to targetable transcriptomic alterations (Fig. 2A).To mitigate our relatively small sample size, we excluded outlier samples that did not cluster transcriptomically with the majority of the samples sharing the same mutated pathway.This allowed us to study relatively more homogeneous groups of samples (Appendix Fig. S2).
We first observed that mutations in the Hippo pathway led to downregulation of the cell cycle and MYC-target, compared to Hippo wild-type samples.Since the cell cycle pathway was upregulated in CDC, independent of Hippo mutations, this implied different underlying mechanisms drive cell cycle progression.Different from the Hippo pathway-mutant tumors, the wild-type tumors showed induced immune-related pathways, such as interferon alpha/gamma response (Fig. 2B,C).As expected, KRAS hotspot mutations led to dysregulation of known KRAS-targeted genes (Fig. 2D,E).Interestingly, patients harboring KRAS mutations had worse survival (Fig. 2F, Cox model, p = 0.0039).Patients with AA-signatures exhibited an upregulated NFκB pathway, consistent with a higher number of CD8+ naive T−cells and Th1 cells (Fig. 2G-I).Because of the co-occurrence of RTK-RAS mutation and AA signature, AA-positive tumors also showed altered expressions in KRAS-targeted genes (Figs. 1B, 2A,H).However, mutations in KRAS are C > G/A/T, different from the typical T > A mutations caused by AA (Poon et al, 2013), indicating the involvement of multiple mutational processes.
Besides pathways related to specific mutations, epithelial and mesenchymal transition (related to cancer invasion) and cell cyclerelated pathways were commonly upregulated in CDC (Fig. 2C,D,H).
Therefore, we hypothesized that, although CDC tumors exhibit heterogeneity in terms of somatic mutations and associated transcriptomic dysregulation, cell cycle-related pathways may be a commonly induced pathway in CDC that is potentially targetable.We next studied cell-cycle pathways in CDC in depth.

Cell cycle pathway is commonly upregulated in both Asian and Caucasian CDC
At the whole transcriptome level, Principal Component Analysis (PCA) showed that CDC tumors (n = 8) were clearly separated n.a.from the normal kidney samples by the first principal component (PC1).CDC1 (PDX) resembled the CDC tumors (Fig. 3A).
Fold changes of differentially expressed genes (DEGs) in our Asian cohort significantly correlated with those in the Msaouel Caucasian cohort (Msaouel et al, 2020a) (Fig. 3C, R = 0.947 and p value = 0.0).There were 835 and 2042 upregulated genes in the two cohorts respectively, of which 615 are common; and there are 1205 and 1917 downregulated genes in the two cohorts, with 910 shared ones (Fig. 3D).
Correlation analysis of commonly upregulated genes identified three tight genes clusters, namely u1 (n = 333), u2 (n = 120), and u3 (n = 162) (Fig. 3E).Similarly, downregulated genes formed a large cluster d1 (n = 910) (Fig. 3F).Gene ontology analysis found that 33.53% of u1 genes were cell cycle-related (Fig. 3G); and 46.28% of the u2 genes were involved in immune system process and 14.11% of the u3 genes were related to signal transduction.On the other hand, the commonly downregulated genes were enriched in transport (23.3%) (Fig. 3H).Further analyses of the cell cycle genes found they were associated with E2F targets, G2-M checkpoint, and mitotic spindle (Fig. 3I).Expression of selected genes, AURKB, CDC45, and TPX2 were validated in nine CDC patient tissues using real-time qRT-PCR (Fig. 3J-L).Therefore, through transcriptome analysis, combined with published data, we found that cell cycle pathways might serve as drivers for CDC tumorigenesis.

Drug screening identified CDK9 inhibitor, LDC000067, specifically suppressed CDC
For drug screening and testing, we established one PDX and primary cell line from patient CDC1's spinal metastasis (51 yr, Chinese, male) (Fig. 4A).CDC1 carries missense mutations in the FAT1 and LATS1 genes of the Hippo pathway (Dataset EV1).To further confirm the identity of the PDX (CDC1) sample as CDC, we first conducted immunohistochemistry (IHC) staining for PAX8 and CK19 in both CDC1 patients and PDX samples.The results showed that strong positive expression of PAX8 and CK19 in both samples, indicating these samples are real CDC (Fig. 4B).We next confirmed that CDC1 cell line transcriptionally resembled CDC tumors, distinct from normal kidneys, though some of the tumors, such as T10, showed relatively lower level of cell-cycle pathways, highlighting tumor heterogeneity (Fig. 4C).The primary cell line CDC1 was screened using the drug library containing 130 compounds (1 μM) targeting cell cycle regulators for 96 h.Five drugs (Indisulam, Rigosertib, LDC000067, THZ1, and Briciclib) had an inhibition rate of at least 70%, and the top ten compounds were mostly CDK inhibitors (Fig. 4D,E).
We then evaluated the drugs' nephrotoxicity and specificity for CDC, by testing them in one normal immortalized kidney cell line (HK-2) and two ccRCC cell lines (A-498 and 786-O) (Fig. 4F).LDC000067, a CDK9 inhibitor, was selected for further validation due to its specific inhibition of CDC1 cell line by 74.86% and low nephrotoxicity with inhibition rate of only 3.15% on HK-2.We further showed that LDC000067 achieved dosage-dependent inhibition of CDC1 with an IC50 of 1.46 μM (Fig. 4G); and treating CDC1 with LDC000067 at 1 μM concentration significantly inhibited the cell growth (Fig. 4H, n = 5, p value = 3.30E-05, two-way ANOVA).
Therefore, LDC000067 can specifically inhibit CDC growth, with little toxicity to the normal kidney cell line.

CDK9 inhibitor LDC000067 improved the survival of the CDC PDX model
After confirming the tumor inhibitory effect of LDC000067 in vitro, we used our PDX model to study its efficacy in vivo.
Compared to the vehicle treatment, LDC000067 significantly suppressed the tumor growth in the mice (Fig. 5A, p value = 1.1E-07, two-way ANOVA), thereby extending the median survival of the mice by 21 days (53.5 vs. 32 days, p value = 3.00E-06, log-rank test, Fig. 5B).Importantly, consistent with in vitro observations, LDC000067 was well tolerated, as reflected by the relatively constant body weight of the mice (Fig. 5C).Ki67 staining showed that LDC000067-treated tumors had significantly fewer number of tumor cells, compared with the vehicle-treated controls (Fig. 5D,E, n = 5, p value = 6.80E-06, two-sided t-test, equal variance).Importantly, induced genes (AURKA and TPX2) in CDC were downregulated upon drug treatment (Fig. 5D,F,G).Reduced p-RB1 staining upon LDC000067 treatment also indicated suppressed cell cycle progression (Fig. 5D,H).
In summary, we successfully demonstrated the tumor suppressive effect of LDC000067 in the PDX model.

Discussion
CDC is an aggressive and rare subtype of kidney cancer with no effective targeted therapy.To date, the etiology of the disease is largely unknown, with patchy evidence for recurrent somatic mutations, primarily discovered in the Caucasian population.To date, only one other genomic study focused on Chinese CDC patients (n = 10) using formalin-fixed, paraffin-embedded (FFPE) samples, which found a similar TMB with ours (1.37 vs our 1.86 per MB) (Zhang et al, 2022a).Zhang et al, identified many focal amplifications and deletions, among which 9p21.3, where CDKN2A and CDKN2B genes are located, is the only focal deletion found in our cohort.However, given the discordant gene expression changes in CDKN2A and CDKN2B, the role of 9p21.3 deletion in CDC tumorigenesis remains to be investigated.We hypothesize the increased CDKN2A and CDKN2B gene expressions were downstream dysregulation caused by mutations in oncogenic pathways, consistent with the induced cell-cycle pathway observed.Such   extensive focal changes in Zhang et al, cohort might be related to the nature of the FFPE samples.In terms of somatic small variants, TP53 was the only common top frequent mutations, but with different frequency (10% versus 23% in our cohort).The low concordance of the genomic changes indicates the heterogeneity of CDC tumorigenesis, which requires orthogonal characterizations, such as transcriptomic profiling.
To our knowledge, our study is the first integrative analysis of matched genome and transcriptome of Asian CDC.With the matched genomic and transcriptomic data, we not only identified novel genomic mutations but also their downstream effects at the transcriptome level.
The genomic landscape of the Asian CDC we described may have important clinical implications.Recurrent TP53 and KRAS hotspot mutations in Asian CDC patients and their associated pathway dysregulations at the transcriptome levels could be exploited for precision therapy.TP53 mutated tumors can potentially be treated with various FDA-approved drugs (Hassin and Oren, 2023).KRAS, a GTPase that regulates cell proliferation, frequently mutates in its codon 12 in multiple cancers, including our current Asian CDC cohort, where the patients were shown to have poor survival outcomes.Therefore, the recently developed KRAS hotspot mutation inhibitor, such as sotorasib (AMG 510) could potentially be used in treating these KRAS-mutant CDC patients (Canon et al, 2019;Kim et al, 2023;Zhang et al, 2022b).Because of our small sample size, and the wide confidence interval, especially for KRAS wild-type patients, how or whether the KRAS hotspot mutations lead to poorer survival requires further investigations.
Oncogenes in our Asian cohort showed different mutation frequencies than the Caucasian in Pal et al, (Pal et al, 2016), although not statistical significant due to sample size (one-sided Fisher's Exact Test).For example, mutation frequency of TP53 (23%, 3/13) is more than triple the 6% (1/17) reported by Pal et al, (p value = 0.20).NF2 mutated in 15% (2/13) of the patients, less than half the 29% (5/17) in Pal et al, (p value = 0.33).KRAS G12A/ D/V hotspot mutations, not reported in Pal et al, occurred in 23% (3/13) of the patients (p value = 0.07).While such difference could be partly contributed by the different genomic backgrounds, environmental factors may play an equally important role, as suggested by the presence of mutational signature SBS42, caused by occupational exposure to haloalkanes.As we do not have occupational data on patients, the role of haloalkane exposure on CDC tumorigenesis requires further study, although it was previously found in printing workers who developed cholangiocarcinoma (Alexandrov et al, 2020).
Interestingly, we also found AA mutational signature (SBS22) in our Asian CDC patients, highlighting a geologically specific disease etiology like other Asian cancers (Das et al, 2022;Senkin et al, 2024).Because of the higher mutational burden and more prominent neoantigen presentations induced by AA exposure, this subgroup of CDC may respond to immune checkpoint blockade therapy, which is also supported by the relatively higher number of CD8+ naïve T−cells and Th1 cells observed in the AA+ tumors in our cohort.
By integrating Asian and Caucasian CDC transcriptome data, we identified that cell cycle, and immune response pathways to be commonly upregulated.Reassuringly, our data are consistent with previously published results (Gargiuli et al, 2021).These findings inform new therapeutic strategies for CDC, through cell cycle inhibition or utilization of immunotherapy; and the efficacy of immunotherapy has been reported in various case reports (Pyrgidis et al, 2023).
To facilitate drug screening and testing, we established CDC cell line and PDX models, as important resources for further drug development.To our knowledge, our PDX model is the first in the world, and the CDC cell line is one of the three cell lines available (Wu et al, 2009).Through drug library screening on our newly established CDC cell line, we found CDK9 inhibitor LDC000067 effectively inhibited the growth of CDC tumors, sparing normal kidney cells.This is the first in vivo validated potential CDC drug candidate.
Notably, epigenomic studies of CDC are still absent.This is mainly because the disease is rare and archived specimens are stored in FFPE (formalin-fixed paraffin-embedded) blocks, on which it is still technically challenging to profile genome-wide histone modifications.
To our knowledge, our study is the first multi-omics study of the Asian CDC.It not only improved our fundamental understanding of molecular alterations in CDC but also identified and validated cell cycle pathway inhibition as a viable therapeutic strategy.However, our CDC cell line model cannot possibly cover the entire heterogeneous spectrum of CDC.Further studies in more preclinical models are required before its clinical usage.Treatment-naïve CDC patients, who underwent radical nephrectomy at the participating institutions, were included with written informed consents, if fresh-frozen tissues were available.CDC diagnosis was confirmed by 2-3 uropathologists.Histological staining, immunochemistry staining, and molecular profiling were used whenever possible to ensure accurate subtyping of the cases.All animal studies were conducted in compliance with animal protocols approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University Cancer Center (Guangzhou, China).The experiments conformed to the principles set out in the WMA Declaration of Helsinki and the Department of Health and Human Services Belmont Report.

Establishment of patient-derived xenograft (PDX) and patientderived CDC cells
To establish the PDX model, female NOD/SCID mice (6-8 weeks old) were purchased from Beijing Vital River Laboratory Animal Technology Company (Beijing, China) and housed under specific pathogen-free conditions in the Laboratory Animal Center of Sun Yat-sen University.Tumor tissue (CDC1, distant spinal metastasis of the left renal collecting duct) was obtained from the First Affiliated Hospital of Sun Yat-sen University with the patient's informed consent, from which one PDX model and primary cell line were established.Specifically, tumor tissue was washed in PBS and minced with a sterile scalpel to patches that could pass through a needle bore.Mice were implanted subcutaneously with pieces of fresh CDC1 tumor tissue.Tumors were excised when they reached 1000 mm 3 to establish the patient-derived CDC cells.H&E staining was performed to confirm the morphology of the CDC tumor.CDC1 tumor cells were disassociated from the CDC1 PDX tumors by using the Tumor Dissociation Kit (Miltenyi Biotec,.To ensure the purity of patient-derived tumor cells, the mouse-derived cells were removed using the Mouse Cell Depletion Kit (Miltenyi Biotec,.The purified CDC1 cells were then seeded and maintained in DMEM/F12 (1:1) medium supplemented with 10% FBS and 1% penicillin/streptomycin.At 80 to 90% confluence, the cells were passaged at a 1:2 ratio.

Genome and transcriptome characterization of samples
Whole-exome (WES) and transcriptome (RNA-seq) sequencing was performed on an Asian CDC cohort (n = 14, 13 normalmatched tumors and 1 PDX model) to identify recurrent somatic mutations, mutational signatures, and associated gene regulatory network changes.CDC RNA-seq data (n = 9) from Msaouel et al, (Msaouel et al, 2020b) was integrated with our transcriptome data to increase sample size and statistical power (Data ref: NCBI Sequence Read Archive PRJNA605003, 2020, normal samples; raw data from CDC tumor samples were provided by Dr. Msaouel).

Drug candidate identification via in vitro screening
For drug screening, CDC1 cells were plated in 96-well plates and treated with 130 small molecules (1 μM) of the cell cycle regulator compound library (TargetMol, cat #L8100) for 96 h.Cell viability was measured using CellTiter Glo reagent (Promega, cat #G7570) according to the manufacturer's instructions.Inhibition rates were calculated as the cell viability of the drug-treated cells normalized to that of the DMSO-treated.Top 10 drug candidates with a high inhibition rate were further tested in one normal immortalized kidney cell line (HK-2), and two ccRCC cell lines (A-498, 786-O) to ensure low nephrotoxicity and drug specificity.

Drug validation using in vivo models
All animal studies were conducted in compliance with animal protocols approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University Cancer Center (Guangzhou, China).Five-to six-week-old NOD/SCID mice were purchased from Beijing Vital River Laboratory Animal Technology Company.
To validate drug efficacy in vivo, CDC1 (PDX) tumor masses were passaged in NOD/SCID mice after subcutaneous implantation.When the tumor volumes reached approximately 100 mm 3 , the mice were divided into two groups for treatment.Randomization was performed by equally dividing the tumor-bearing mice with a similar tumor burden into groups for drug treatment.The CDK9 inhibitor LDC000067 (MedChemExpress, cat # HY-15878) was suspended in 1 × saline and was given by oral gavage for 21 days (10 mg/kg daily) with blinding.Tumor volume and body weight were monitored every two days until the tumor volume reached 1000 mm 3 .Mice were sacrificed by CO 2 inhalation and the survival time of each mouse was recorded.

For more information
• https://rarediseases.info.nih.gov/diseases/9573/collecting-ductcarcinoma• https://www.orpha.net/en/disease/detail/247203• https://www.malacards.org/card/collecting_duct_carcinoma The paper explained Problem Collecting duct carcinoma (CDC) remains a lethal and aggressive form of kidney cancer with no effective treatment, primarily because of our limited understanding of its underlying molecular alterations and etiology.The rarity of the CDC and the lack of preclinical models, particularly for Asian patients, have hindered scientific and clinical progress in this field.This study aimed to comprehensively characterize the molecular alterations of CDC and establish preclinical models for drug screening and testing, to identify potentially targetable therapeutic vulnerabilities.

Results
Through genome and transcriptome sequencing, we identified frequent KRAS hotspot mutations, in addition to known TP53 and NF2 mutations.Notably, the mutational signature SBS22 caused by aristolochic acid (AA) exposure was detected in 3/13 patients.The presence of SBS22, known to be more prevalent in Asia, highlights a geologically specific etiology of the disease.We further found that cell cycle-related pathways were highly dysregulated.Using our newly established CDC preclinical models, coupled with drug screening, we identified a CDK9 inhibitor, LDC000067, as a promising candidate which could specifically target CDC tumor growth and extend survival.

Impact
Our study demonstrated that targeting cell-cycle machinery effectively suppressed CDC tumor growth, laying a solid foundation for initiating clinical trials aimed at treating patients affected by this highly aggressive cancer.

Figure 1 .
Figure 1.Genomic landscape of collecting duct carcinoma.(A) Tumor mutation burden (TMB).(B) Mutations in oncogenic pathways and percentage of samples in which the pathways are mutated in (left).(C) Demographic information of the patients.(D) Single base substitutions (SBS) Class.(E) Mutational signature (%) in each patient.Cosine similarity: accuracy metric between 0 and 1 for the reconstruction of the original mutational catalog.(F) Neoantigen (strong binder) prediction.Source data are available online for this figure.

Figure 2 .
Figure 2. Genomic mutation and associated transcriptomic alteration inform precision therapy.(A) Overlapping of patients with different oncogenic mutations.Numbers in brackets indicate the number of cases with matched RNA-seq data.(B) Gene set enrichment analysis (GSEA), comparing Hippo pathway mutant (mut) and wild-type (wt) samples.(C) Enrichment of differentially expressed genes between Hippo mut, Hippo wt tumor, and normal samples.Hypergeometric test with Benjamini-Hochberg (BH) adjustment, where reference background is the total number of genes in the Hallmark collection.(D) Enrichment of differentially expressed genes between RTK-RAS pathway mutant (mut) and wild-type (wt) samples, as well as normal samples.Hypergeometric test with Benjamini-Hochberg (BH) adjustment, where reference background is the total number of genes in the Hallmark collection.(E) Heatmap showing expression levels of genes upregulated in HALLMARK_KRAS_SIGNALING_UP gene set, comparing RTK-RAS mutant (mut) with wild-type (wt) tumors.(F) Kaplan-Meier plot based on KRAS genotype (wt: patients with wild-type KRAS, n = 6; mut, patients with KRAS mutation, n = 2).P value: log-rank test.(G) Whole transcriptome of AA-positive (+) and AA-negative (−) samples.Dist.: Poisson dissimilarity matrix.(H) Enrichment of differentially expressed genes between AA-positive (+), AA-negative (−) and normal samples.Hypergeometric test with Benjamini-Hochberg (BH) adjustment, where reference background is the total number of genes in the Hallmark collection.(I) Cell type enrichment analysis.Only those showed significant differences between AA-positive (n = 2) and AA-negative (n = 3) samples are shown (one tail t-test, p value ≤0.05).p = 1.37E-02, 1.50E-02, 1.17E-04, and 1.04E-02 for CD8+ naive T-cells, Epithelial cells, Keratinocytes, and Th1 cells respectively.Source data are available online for this figure.

Figure 3 .
Figure 3. Identifying drivers for CDC tumorigenesis by transcriptomic profiling.(A) Principal component analysis (PCA) of Asian cohort (n = 8 pairs).(B) Gene set enrichment analysis (GSEA), top five enriched Hallmark pathways shown.(C) Correlation of log2 fold changes (log2FC) in Asian cohort and Msaouel Caucasian cohort.R: Pearson correlation coefficient; p < 2.2E-16: correlation test p value.(D) Overlapping of differentially expressed genes in Asian cohort and Msaouel Caucasian cohort.Cutoff values: p value ≤0.01 and |log2FC|≥ 2. P values for overlapping of up-/down-regulated genes in Asian and Caucasian cohorts <2.2E-16, Fisher's Exact Test.(E) Correlation coefficient (Spearman, squared) of the z-score normalized gene expression levels of commonly upregulated genes.(F) Correlation coefficient (Spearman, squared) of the z-score normalized gene expression levels of commonly downregulated genes.(G) Gene ontology analysis for commonly upregulated gene clusters.(H) Gene ontology analysis for commonly downregulated genes.(I)Subcategorizing commonly upregulated cell cycle genes into different processes.(J-L) Real-time qRT-PCR (real-time quantitative reverse transcription PCR) validation of selected genes, AURKB, CDC45, and TPX2.For each sample, data were representative of three independent experiments.Data were presented as the mean ± SD.P value: paired t-test of average log2 of relative mRNA level (n = 3).Source data are available online for this figure.

Figure 4 .
Figure 4. CDK9 inhibitor LDC000067 specifically suppressed CDC rather than other RCC.(A) Workflow for establishing PDX and primary cell line; and screening for potential drug targets.(B) Hematoxylin and eosin (H&E), CK19, and PAX8 staining of CDC1 patient and PDX samples.(C) Single-sample GSEA (ssGSEA) score for all RNA-seq samples.The top 15 cell types (ranked by the variance of ssGSEA scores) are shown.CDC1 (PDX) clusters together with tumors, different from normal samples.(D) Overall ranking of the 130 small molecule compounds in the drug library by their inhibition rates.(E) Top ten drug candidates with the highest inhibition rates.(F) Testing of the top ten drug candidates at 1 µM concentration in clear cell RCC cell lines (A-498 and 786-O) and normal immortalized kidney cell line (HK-2).(G) Dosage-dependent response of CDC1 to CDK9 inhibitor LDC000067 and its IC50s for different cell lines.The cells were treated with LDC000067 for 96 h.(H) Relative growth rate of CDC1 when treated with LDC000067 at 1 µM concentration.Data were presented as the mean ± SD (n = 3).P value = 3.30E-05, two-way ANOVA.Source data are available online for this figure.

Figure 5 .
Figure 5. CDK9 inhibitor LDC000067 prolonged the survival in the CDC PDX model.(A) Tumor volumes for CDC PDX models treated with vehicle control (n = 9 mice) or CDK9 inhibitor LDC000067 (LDC, n = 10 mice) (p value = 1.1E-07, two-way ANOVA).Data were presented as the mean ± SD. (B) Survival analysis for mice treated with LDC (n = 10 mice), compared with those treated with vehicle control (n = 9 mice) (p value = 3.00E-06, log-rank test).(C) Body weight of the CDC1 PDXs.Vehicle control (n = 9 mice) and LDC (n = 10 mice).Data were presented as the mean ± SD. (D) Ki67, p-AURKA, TPX2, and p-RB1 staining of selected PDX tumors treated with vehicle control or LDC.(E-H) Ki67 positive cells per area, a staining score of p-AURKA, TPX2, and p-RB1 respectively, comparing PDX tumors treated with vehicle control or LDC (n = 5 mice, two-sided t-test, equal variance).Source data are available online for this figure.